#Libraries to be installed
##!pip -q is to give less output
# !pip -q install sodapy
# !pip -q install seaborn
# !pip -q install pandas
# !pip -q install matplotlib
# !pip -q install numpy
# !pip -q install nbconvert
# !pip -q install keyboard
# !pip -q install geopandas
# !pip -q install sklearn
# !pip -q install folium

#load libraries
import os
import time

from datetime import datetime
import numpy as np
import pandas as pd
from sodapy import Socrata
import seaborn as sns

from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA

import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib import style
style.use('ggplot')

import plotly.graph_objs as go
import plotly.express as px

import warnings
warnings.filterwarnings('ignore')

import folium
from folium.plugins import MarkerCluster

import requests

#Pedestrian sensor location data
#This URL is created from v2 API of CoM open data
url = 'https://data.melbourne.vic.gov.au/api/v2/catalog/datasets/pedestrian-counting-system-sensor-locations/exports/json?limit=-1&offset=0&timezone=UTC'

#Use requests to get json response
r = requests.get(url)
response = r.json()

#Store and output in dataframe
sensor_location = pd.DataFrame(response)
sensor_location.head(5)

# Rename header to match the below dataset
sensor_location = sensor_location.rename(columns={'location_id': 'sensor_id'})

# Alter lat and long for graphing
sensor_location[['latitude', 'longitude']] = sensor_location[['latitude', 'longitude']].astype(float)
sensor_location = sensor_location.drop('location',axis=1)
sensor_location['lat'] = sensor_location['latitude'].apply(lambda x: float(x))
sensor_location['lon'] = sensor_location['longitude'].apply(lambda x: float(x))

# **** IMPORTANT ***
# This dataset is not available through API v2
# It must be downloaded via CSV which is saved locally
# Please ensure when merging future code, that the CSV file created when using this use case is not in the directory

from urllib.request import urlopen
from io import BytesIO
from zipfile import ZipFile

url = 'https://data.melbourne.vic.gov.au/api/datasets/1.0/pedestrian-counting-system-monthly-counts-per-hour/attachments/pedestrian_counting_system_monthly_counts_per_hour_may_2009_to_14_dec_2022_csv_zip/'

def download_and_unzip(url, extract_to='.'):
    http_response = urlopen(url)
    zipfile = ZipFile(BytesIO(http_response.read()))
    zipfile.extractall(path=extract_to)
    
# *** PLEASE UNCOMMENT BELOW TO EXECUTE CSV SAVE ***    

# download_and_unzip(url)

# sensor_traffic = pd.read_csv('Pedestrian_Counting_System_Monthly_counts_per_hour_may_2009_to_14_dec_2022.csv')

# sensor_traffic = sensor_traffic.rename(columns=str.lower)

# sensor_traffic.head(10)

#Add date column
sensor_traffic['date'] = pd.to_datetime(sensor_traffic.date_time).dt.date

#Add day of week column
sensor_traffic['dow'] = pd.to_datetime(sensor_traffic.date_time).dt.day_of_week

#convert sensor_id to integer
sensor_traffic['sensor_id']=sensor_traffic['sensor_id'].astype(int)
sensor_location['sensor_id']=sensor_location['sensor_id'].astype(int)

# Mesh pedestrian sensor location and foot traffic datasets
sensor_ds = pd.merge(sensor_traffic, sensor_location, on='sensor_id')

#Simply using the year to differentiate all the years prior to 2020 as before Covid, and post 2019 Covid traffic to now
sensor_ds['pre2020_hourly_counts'] = np.where(sensor_ds['year']<2020,sensor_ds['hourly_counts'] , 0)
sensor_ds['post2019_hourly_counts'] = np.where(sensor_ds['year']>2019,sensor_ds['hourly_counts'] , 0)

#Add column for day or night traffic
#Add column for day (5am to 5pm) or night (6pm to 4am) traffic
sensor_ds['day_counts']   = np.where(((sensor_ds['time']>4)  & (sensor_ds['time']<18)),sensor_ds['hourly_counts'] , 0)
sensor_ds['night_counts'] = np.where(sensor_ds['day_counts']==0,sensor_ds['hourly_counts'], 0)

sensor_ds.describe()

flag_value=0
df_day=sensor_ds.query("day_counts > @flag_value")
print('Day info\n', df_day.info(),'\n')

df_night=sensor_ds.query("day_counts == @flag_value")
print('Night info \n',df_night.info(),'\n')

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2221632 entries, 0 to 4128864
Data columns (total 28 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   id                      int64  
 1   date_time               object 
 2   year                    int64  
 3   month                   object 
 4   mdate                   int64  
 5   day                     object 
 6   time                    int64  
 7   sensor_id               int64  
 8   sensor_name_x           object 
 9   hourly_counts           int64  
 10  date                    object 
 11  dow                     int64  
 12  sensor_description      object 
 13  sensor_name_y           object 
 14  installation_date       object 
 15  note                    object 
 16  location_type           object 
 17  status                  object 
 18  direction_1             object 
 19  direction_2             object 
 20  latitude                float64
 21  longitude               float64
 22  lat                     float64
 23  lon                     float64
 24  pre2020_hourly_counts   int64  
 25  post2019_hourly_counts  int64  
 26  day_counts              int64  
 27  night_counts            int64  
dtypes: float64(4), int64(11), object(13)
memory usage: 491.5+ MB
Day info
 None 

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1907239 entries, 1 to 4128870
Data columns (total 28 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   id                      int64  
 1   date_time               object 
 2   year                    int64  
 3   month                   object 
 4   mdate                   int64  
 5   day                     object 
 6   time                    int64  
 7   sensor_id               int64  
 8   sensor_name_x           object 
 9   hourly_counts           int64  
 10  date                    object 
 11  dow                     int64  
 12  sensor_description      object 
 13  sensor_name_y           object 
 14  installation_date       object 
 15  note                    object 
 16  location_type           object 
 17  status                  object 
 18  direction_1             object 
 19  direction_2             object 
 20  latitude                float64
 21  longitude               float64
 22  lat                     float64
 23  lon                     float64
 24  pre2020_hourly_counts   int64  
 25  post2019_hourly_counts  int64  
 26  day_counts              int64  
 27  night_counts            int64  
dtypes: float64(4), int64(11), object(13)
memory usage: 422.0+ MB
Night info 
 None

#split dataset to map difference between time before covid-19 and time after covid-19
df = df_day
print(df_day.head(2))

df_daypft_beforecovid = df.loc[df['year']<2020]
df_daypft_aftercovid = df.loc[df['year']>2019]

#get average hourly count for each sensor during the selected period of time
df_daypft_beforecovid_avg = df_daypft_beforecovid[['sensor_id','sensor_description','lat','lon','hourly_counts']]
df_daypft_beforecovid_avg = df_daypft_beforecovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'hourly_counts': 'mean'})
df_daypft_aftercovid_avg = df_daypft_aftercovid[['sensor_id','sensor_description','lat','lon','hourly_counts']]
df_daypft_aftercovid_avg = df_daypft_aftercovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'hourly_counts': 'mean'})

         id                      date_time  year     month  mdate       day  \
0   2887629  November 01, 2019 05:00:00 PM  2019  November      1    Friday   
12  2888289  November 02, 2019 05:00:00 AM  2019  November      2  Saturday   

    time  sensor_id sensor_name_x  hourly_counts  ... direction_1  \
0     17         39  Alfred Place            604  ...       North   
12     5         39  Alfred Place              9  ...       North   

    direction_2   latitude   longitude        lat         lon  \
0         South -37.813797  144.969957 -37.813797  144.969957   
12        South -37.813797  144.969957 -37.813797  144.969957   

   pre2020_hourly_counts post2019_hourly_counts day_counts night_counts  
0                    604                      0        604            0  
12                     9                      0          9            0  

[2 rows x 28 columns]

#split dataset to see difference between time before covid-19 and time after covid-19
df = df_night
print(df.head(2))

df_night_beforecovid = df.loc[df['year']<2020]
df_night_aftercovid = df.loc[df['year']>2019]

#get average hourly count for each sensor during the selected period of time
df_night_beforecovid_avg = df_night_beforecovid[['sensor_id','sensor_description','lat','lon','pre2020_hourly_counts']]
df_night_beforecovid_avg = df_night_beforecovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'pre2020_hourly_counts': 'mean'})
df_night_aftercovid_avg = df_night_aftercovid[['sensor_id','sensor_description','lat','lon','post2019_hourly_counts']]
df_night_aftercovid_avg = df_night_aftercovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'post2019_hourly_counts': 'mean'})

        id                      date_time  year     month  mdate     day  \
1  2887684  November 01, 2019 06:00:00 PM  2019  November      1  Friday   
2  2887739  November 01, 2019 07:00:00 PM  2019  November      1  Friday   

   time  sensor_id sensor_name_x  hourly_counts  ... direction_1  direction_2  \
1    18         39  Alfred Place            384  ...       North        South   
2    19         39  Alfred Place            289  ...       North        South   

    latitude   longitude        lat         lon pre2020_hourly_counts  \
1 -37.813797  144.969957 -37.813797  144.969957                   384   
2 -37.813797  144.969957 -37.813797  144.969957                   289   

  post2019_hourly_counts day_counts night_counts  
1                      0          0          384  
2                      0          0          289  

[2 rows x 28 columns]

#examine pre Covid and post 2019 foot traffic
ds = pd.DataFrame(sensor_ds.groupby(["time"])["pre2020_hourly_counts","post2019_hourly_counts"].mean())
df = ds.sort_values(by=['time'])
axs = df.plot.line(figsize=(20, 6), color=["#0f9295","orange"])
axs.set_title('Foot Traffic by Time', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()

#distribution by traffic, by day
pivot = pd.DataFrame(pd.pivot_table(sensor_ds, values=['day_counts','night_counts'], index=['year'], aggfunc=np.mean))
rs = pivot.sort_values(by='year', ascending = False)
axs = rs.plot.line(figsize=(12, 5), color=['orange','#023545'], legend=True);

axs.set_title('Foot traffic by year by day and night', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()

#examine pre Covid and post 2019 foot traffic
ds = pd.DataFrame(sensor_ds.groupby(["dow","day"])["pre2020_hourly_counts","post2019_hourly_counts"].mean())
df = ds.sort_values(by=['dow'])
axs = df.plot.bar(figsize=(12, 4), color=["#0f9295","orange"])
axs.set_title('Foot Traffic by Day of Week - All Hours', size=20)
axs.set_ylabel('Average hourly counts', size=14)
plt.show()

#distribution by traffic, by day
pivot = pd.pivot_table(sensor_ds, values='day_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['day_counts'].nlargest(n=20)
pivot_ds.plot.bar(figsize=(12, 5), color='orange', legend=True);

#by night
pivot = pd.pivot_table(sensor_ds, values='night_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['night_counts'].nlargest(n=20)
axs = pivot_ds.plot.bar(figsize=(12, 5), color='#023545', legend=True);

axs.set_title('Top 20: Foot Traffic by location by day and night', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()

#distribution by traffic, by day
pivot = pd.pivot_table(df_day, values='pre2020_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['pre2020_hourly_counts'].nlargest(n=20)
pivot_ds.plot.line(figsize=(12, 5), color='#0f9295', legend=True, rot=90);

#by night
pivot = pd.pivot_table(df_day, values='post2019_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['post2019_hourly_counts'].nlargest(n=20)
axs = pivot_ds.plot.bar(figsize=(12, 5), color='orange', legend=True, rot=90);

axs.set_title('Top 20: Foot Traffic by location by day', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()

#Visualise day data
m = folium.Map(location=[-37.8167, 144.967], zoom_start=15)# tiles='Stamen Toner'
locations = []
for i in range(len(df_daypft_beforecovid_avg)):
    row =df_daypft_beforecovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='day traffic before covid-19',
  locations=locations,
  overlay=True,
  control=True,
  color='red',
  show=False # this removes from automatic selection in display - need to select to show data points
  )
marker_cluster.add_to(m)

#next layer
locations = []
for i in range(len(df_daypft_aftercovid_avg)):
    row =df_daypft_aftercovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='day traffic after covid-19',
  locations=locations,
  overlay=True,
  control=True,
  ) 
marker_cluster.add_to(m)
folium.LayerControl().add_to(m)
m

#identify class and features
use_list = ["sensor_id","lat","lon","hourly_counts"]
loc_day = pd.DataFrame(df_day[use_list])
print(loc_day.head(5))

codes = loc_day[['sensor_id']] #class
loc_day.drop('sensor_id', axis=1, inplace=True)

loc_day.head(5)

    sensor_id        lat         lon  hourly_counts
0          39 -37.813797  144.969957            604
12         39 -37.813797  144.969957              9
13         39 -37.813797  144.969957             28
14         39 -37.813797  144.969957             53
15         39 -37.813797  144.969957             99

#examine data
loc_day.describe()

# identify optimal clusters 
features_list = ["lat","lon","hourly_counts"]
df_features = pd.DataFrame(loc_day[features_list])

#scale the features
mms = MinMaxScaler()
mms.fit(df_features) #day hours
data_transformed = mms.transform(df_features)

#data_transformed

# for each k value calculate sum of squared distances to the nearest cluster centre
Sum_of_squared_distances = []
K = range(1,10)
for k in K:
    km = KMeans(n_clusters=k)
    km = km.fit(data_transformed)
    Sum_of_squared_distances.append(km.inertia_)
    
#plot results
plt.plot(K, Sum_of_squared_distances, 'g*-')
plt.xlabel('Number of Clusters')
plt.ylabel('Sum of squared distances')
plt.title('Elbow Method')
plt.show()

# run k-means clustering, define centroids
kmeans = KMeans(n_clusters=3, random_state=0).fit(loc_day)
centroids = kmeans.cluster_centers_

codes['cluster'] = kmeans.labels_
#codes.head()

df = df_features

#visualise data
x_axis = df['lon']
y_axis = df['lat']
cluster_legend = kmeans.labels_.astype(float)
plt.figure(figsize = (8,5))
sns.scatterplot(x_axis, y_axis, hue=cluster_legend, c=cluster_legend, palette='colorblind', s=60)
plt.title('Clustering Pedestrian Counts: Day Hours')
plt.show()

#distribution by traffic
#by night before covid
pivot = pd.pivot_table(df_night_beforecovid, values='pre2020_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['pre2020_hourly_counts'].nlargest(n=20)
pivot_ds.plot.bar(figsize=(12, 5), color='#0f9295', legend=True);

#by night after Covid
pivot = pd.pivot_table(df_night_aftercovid, values='post2019_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['post2019_hourly_counts'].nlargest(n=20)
axs = pivot_ds.plot.bar(figsize=(12, 5), color='#023545', legend=True);

axs.set_title('Top 20: Foot Traffic by location by night, before and after Covid', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()

#Visualise night data
m = folium.Map(location=[-37.8167, 144.967], zoom_start=15)
locations = []
for i in range(len(df_night_beforecovid_avg)):
    row =df_night_beforecovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['pre2020_hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='Night traffic before Covid-19',
  locations=locations,
  overlay=True,
  control=True,
  show=False # this removes from automatic selection in display - need to select to show data points
  )
marker_cluster.add_to(m)
locations = []
for i in range(len(df_night_aftercovid_avg)):
    row =df_night_aftercovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['post2019_hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='Night traffic after Covid-19',
  locations=locations,
  overlay=True,
  control=True,
  ) 
marker_cluster.add_to(m)
folium.LayerControl().add_to(m)

<folium.map.LayerControl at 0x167dcfb80>

m

#identify class and features
use_list = ["sensor_id","lat","lon","hourly_counts"]
loc_night = pd.DataFrame(df_night[use_list])
#print(loc_night.head(5))

codes = loc_night[['sensor_id']] #class
loc_night.drop('sensor_id', axis=1, inplace=True)

loc_night.head(5)

# identify optimal clusters - these may be the same or different to day
features_list = ["lat","lon","hourly_counts"]
df_features = pd.DataFrame(loc_night[features_list])

#scale the features
mms = MinMaxScaler()
mms.fit(df_features) #day hours
data_transformed = mms.transform(df_features)

#data_transformed

# for each k value calculate sum of squared distances to the nearest cluster centre
Sum_of_squared_distances = []
K = range(1,10)
for k in K:
    km = KMeans(n_clusters=k)
    km = km.fit(data_transformed)
    Sum_of_squared_distances.append(km.inertia_)
    
#plot results
plt.plot(K, Sum_of_squared_distances, 'g*-')
plt.xlabel('Number of Clusters')
plt.ylabel('Sum of squared distances')
plt.title('Elbow Method')
plt.show()

# run k-means clustering, define centroids
kmeans = KMeans(n_clusters=3, random_state=0).fit(loc_night)
centroids = kmeans.cluster_centers_

codes['cluster'] = kmeans.labels_
#codes.head()

df = df_features

#visualise data
x_axis = df['lon']
y_axis = df['lat']
cluster_legend = kmeans.labels_.astype(int)
plt.figure(figsize = (8,5))
axs = sns.scatterplot(x_axis, y_axis, hue=cluster_legend, c=cluster_legend, palette='colorblind', s=60)
plt.title('Clustering Pedestrian Counts: Night Hours')
plt.show()

#save notebook, required so that step to convert to html, writes latest results to file
# may need to adapt for other OS, this is for Windows
# keyboard.press_and_release('ctrl+s')

# !jupyter nbconvert  usecase_evaluate_business_location_using_pedestrian_traffic_day_night.ipynb --to html

	location_id	sensor_description	sensor_name	installation_date	note	location_type	status	direction_1	direction_2	latitude	longitude	location
0	2	Bourke Street Mall (South)	Bou283_T	2009-03-30	None	Outdoor	A	East	West	-37.813807	144.965167	{'lon': 144.96516718, 'lat': -37.81380668}
1	4	Town Hall (West)	Swa123_T	2009-03-23	None	Outdoor	A	North	South	-37.814880	144.966088	{'lon': 144.9660878, 'lat': -37.81487988}
2	6	Flinders Street Station Underpass	FliS_T	2009-03-25	Upgraded on 8/09/21	Outdoor	A	North	South	-37.819117	144.965583	{'lon': 144.96558255, 'lat': -37.81911705}
3	8	Webb Bridge	WebBN_T	2009-03-24	None	Outdoor	A	North	South	-37.822935	144.947175	{'lon': 144.9471751, 'lat': -37.82293543}
4	10	Victoria Point	BouHbr_T	2009-04-23	None	Outdoor	A	East	West	-37.818765	144.947105	{'lon': 144.94710545, 'lat': -37.81876474}

	id	year	mdate	time	sensor_id	hourly_counts	dow	latitude	longitude	lat	lon	pre2020_hourly_counts	post2019_hourly_counts	day_counts	night_counts
count	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06	4.128871e+06
mean	2.367206e+06	2.017557e+03	1.574860e+01	1.147254e+01	2.783812e+01	4.645136e+02	3.001120e+00	-3.781332e+01	1.449616e+02	-3.781332e+01	1.449616e+02	3.653186e+02	9.919500e+01	3.360644e+02	1.284492e+02
std	1.316327e+06	3.542266e+00	8.798729e+00	6.936793e+00	2.051895e+01	7.099215e+02	2.000348e+00	6.473498e-03	8.685007e-03	6.473498e-03	8.685007e-03	6.966499e+02	3.018989e+02	6.724927e+02	3.715865e+02
min	1.000000e+00	2.009000e+03	1.000000e+00	0.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	-3.782402e+01	1.449297e+02	-3.782402e+01	1.449297e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	1.241622e+06	2.015000e+03	8.000000e+00	5.000000e+00	1.000000e+01	4.100000e+01	1.000000e+00	-3.781874e+01	1.449564e+02	-3.781874e+01	1.449564e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
50%	2.411161e+06	2.018000e+03	1.600000e+01	1.100000e+01	2.400000e+01	1.710000e+02	3.000000e+00	-3.781381e+01	1.449644e+02	-3.781381e+01	1.449644e+02	4.100000e+01	0.000000e+00	2.300000e+01	0.000000e+00
75%	3.508766e+06	2.021000e+03	2.300000e+01	1.700000e+01	4.300000e+01	5.610000e+02	5.000000e+00	-3.781102e+01	1.449668e+02	-3.781102e+01	1.449668e+02	3.900000e+02	4.200000e+01	3.320000e+02	5.700000e+01
max	4.567701e+06	2.022000e+03	3.100000e+01	2.300000e+01	8.700000e+01	1.597900e+04	6.000000e+00	-3.779432e+01	1.449747e+02	-3.779432e+01	1.449747e+02	1.597900e+04	1.443700e+04	1.161200e+04	1.597900e+04

	lat	lon	hourly_counts
count	2.221632e+06	2.221632e+06	2.221632e+06
mean	-3.781333e+01	1.449617e+02	6.245708e+02
std	6.468232e-03	8.673052e-03	8.125889e+02
min	-3.782402e+01	1.449297e+02	1.000000e+00
25%	-3.781874e+01	1.449564e+02	1.020000e+02
50%	-3.781381e+01	1.449644e+02	2.880000e+02
75%	-3.781102e+01	1.449668e+02	8.020000e+02
max	-3.779432e+01	1.449747e+02	1.161200e+04

Load Pedestrian sensor location data¶

Load Pedestrian monthly hourly count data¶

Merge Pedestrian sensor location and traffic data¶

Separate day and night data sets¶

Compare day traffic before Covid and now¶

Clustering: Day Traffic¶

The exploratory data analysis on day and night showing initial findings is available, please refer to eda compare pedestrian traffic day night ¶

Compare night traffic before Covid and now¶

Clustering: Night Traffic¶

For night traffic we will try using 3 clusters¶

	id	date_time	year	month	mdate	day	time	sensor_id	sensor_name	hourly_counts
0	2887628	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	34	Flinders St-Spark La	300
1	2887629	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	39	Alfred Place	604
2	2887630	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	37	Lygon St (East)	216
3	2887631	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	40	Lonsdale St-Spring St (West)	627
4	2887632	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	36	Queen St (West)	774
5	2887633	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	29	St Kilda Rd-Alexandra Gardens	644
6	2887634	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	42	Grattan St-Swanston St (West)	453
7	2887635	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	43	Monash Rd-Swanston St (West)	387
8	2887636	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	44	Tin Alley-Swanston St (West)	27
9	2887637	November 01, 2019 05:00:00 PM	2019	November	1	Friday	17	35	Southbank	2691

	lat	lon	hourly_counts
0	-37.813797	144.969957	604
12	-37.813797	144.969957	9
13	-37.813797	144.969957	28
14	-37.813797	144.969957	53
15	-37.813797	144.969957	99

Load Pedestrian sensor location data¶

Load Pedestrian monthly hourly count data¶

Merge Pedestrian sensor location and traffic data¶

Separate day and night data sets¶

Compare day traffic before Covid and now¶

Clustering: Day Traffic¶

The exploratory data analysis on day and night showing initial findings is available, please refer to eda compare pedestrian traffic day night¶

Compare night traffic before Covid and now¶

Clustering: Night Traffic¶

For night traffic we will try using 3 clusters¶

The exploratory data analysis on day and night showing initial findings is available, please refer to eda compare pedestrian traffic day night ¶